Effects of increased image noise on image quality and quantitative interpretation in brain CT perfusion

An automated technique for global noise level measurement in CT image with a conjunction of image gradient

Automated assessment of noise level in clinical computed tomography (CT) images is a crucial technique for evaluating and ensuring the quality of these images. There are various factors that can impact CT image noise, such as statistical noise, electronic noise, structure noise, texture noise, artifact noise, etc. In this study, a method was developed to measure the global noise index (GNI) in clinical CT scans due to the fluctuation of x-ray quanta. Initially, a noise map is generated by sliding a 10 × 10 pixel for calculating Hounsfield unit (HU) standard deviation and the noise map is further combined with the gradient magnitude map. By employing Boolean operation, pixels with high gradients are excluded from the noise histogram generated with the noise map. By comparing the shape of the noise histogram from this method with Christianson's tissue-type global noise measurement algorithm, it was observed that the noise histogram computed in anthropomorphic phantoms had a similar shape with a close GNI value. In patient CT images, excluding the HU deviation due the structure change demonstrated to have consistent GNI values across the entire CT scan range with high heterogeneous tissue compared to the GNI values using Christianson's tissue-type method. The proposed GNI was evaluated in phantom scans and was found to be capable of comparing scan protocols between different scanners. The variation of GNI when using different reconstruction kernels in clinical CT images demonstrated a similar relationship between noise level and kernel sharpness as observed in uniform phantom: sharper kernel resulted in noisier images. This indicated that GNI was a suitable index for estimating the noise level in clinical CT images with either a smooth or grainy appearance. The study's results suggested that the algorithm can be effectively utilized to screen the noise level for a better CT image quality control.

Dataset on renal tumor diameter assessment by multiple observers in normal-dose and low-dose CT

Computed tomography-based active surveillance is increasingly used to manage small renal tumors, regardless of patient age. However, there is an unmet need for decreasing radiation exposure while maintaining the necessary accuracy and reproducibility in radiographic measurements, allowing for detecting even minor changes in renal mass size. In this article, we present supplementary data from a multiobserver investigation. We explored the accuracy and reproducibility of low-dose CT (75% dose reduction) compared to normal-dose CT in assessing maximum axial renal tumor diameter. Open-access CT datasets from the 2019 Kidney and Kidney Tumor Segmentation Challenge were used. A web-based platform for assessing observer performance was used by six radiologist observers to obtain and provide data on tumor diameters and accompanying viewing settings, in addition to key images of each measurement and an interactive module for exploring diameter measurements. These data can serve as a baseline and inform future studies investigating and validating lower-dose CT protocols for active surveillance of small renal masses.

Impact of Noise Level on the Accuracy of Automated Measurement of CT Number Linearity on ACR CT and Computational Phantoms

Background

Methods for segmentation, i.e., Full-segmentation (FS) and Segmentation-rotation (SR), are proposed for maintaining Computed Tomography (CT) number linearity. However, their effectiveness has not yet been tested against noise.

Objective

This study aimed to evaluate the influence of noise on the accuracy of CT number linearity of the FS and SR methods on American College of Radiology (ACR) CT and computational phantoms.

Material and Methods

This experimental study utilized two phantoms, ACR CT and computational phantoms. An ACR CT phantom was scanned by a 128-slice CT scanner with various tube currents from 80 to 200 mA to acquire various noises, with other constant parameters. The computational phantom was added by different Gaussian noises between 20 and 120 Hounsfield Units (HU). The CT number linearity was measured by the FS and SR methods, and the accuracy of CT number linearity was computed on two phantoms.

Results

The two methods successfully segmented both phantoms at low noise, i.e., less than 60 HU. However, segmentation and measurement of CT number linearity are not accurate on a computational phantom using the FS method for more than 60-HU noise. The SR method is still accurate up to 120 HU of noise.

Conclusion

The SR method outperformed the FS method to measure the CT number linearity due to its endurance in extreme noise.

Basis and current state of computed tomography perfusion imaging: a review

Computed tomography perfusion (CTP) is a functional imaging that allows for providing capillary-level hemodynamics information of the desired tissue in clinics. In this paper, we aim to offer insight into CTP imaging which covers the basics and current state of CTP imaging, then summarize the technical applications in the CTP imaging as well as the future technological potential. At first, we focus on the fundamentals of CTP imaging including systematically summarized CTP image acquisition and hemodynamic parameter map estimation techniques. A short assessment is presented to outline the clinical applications with CTP imaging, and then a review of radiation dose effect of the CTP imaging on the different applications is presented. We present a categorized methodology review on known and potential solvable challenges of radiation dose reduction in CTP imaging. To evaluate the quality of CTP images, we list various standardized performance metrics. Moreover, we present a review on the determination of infarct and penumbra. Finally, we reveal the popularity and future trend of CTP imaging.

Cerebral CT Perfusion in Acute Stroke: The Effect of Lowering the Tube Load and Sampling Rate on the Reproducibility of Parametric Maps

The aim of this study was to define lower dose parameters (tube load and temporal sampling) for CT perfusion that still preserve the diagnostic efficiency of the derived parametric maps. Ninety stroke CT examinations from four clinical sites with 1 s temporal sampling and a range of tube loads (mAs) (100–180) were studied. Realistic CT noise was retrospectively added to simulate a CT perfusion protocol, with a maximum reduction of 40% tube load (mAs) combined with increased sampling intervals (up to 3 s). Perfusion maps from the original and simulated protocols were compared by: (a) similarity using a voxel-wise Pearson’s correlation coefficient r with in-house software; (b) volumetric analysis of the infarcted and hypoperfused volumes using commercial software. Pearson’s r values varied for the different perfusion metrics from 0.1 to 0.85. The mean slope of increase and cerebral blood volume present the highest r values, remaining consistently above 0.7 for all protocol versions with 2 s sampling interval. Reduction of the sampling rate from 2 s to 1 s had only modest impacts on a TMAX volume of 0.4 mL (IQR −1–3) (p = 0.04) and core volume of −1.1 mL (IQR −4–0) (p < 0.001), indicating dose savings of 50%, with no practical loss of diagnostic accuracy. The lowest possible dose protocol was 2 s temporal sampling and a tube load of 100 mAs.

Development of a computational phantom for validation of automated noise measurement in CT images

The purpose of this study was to develop a computational phantom for validation of automatic noise calculations applied to all parts of the body, to investigate kernel size in determining noise, and to validate the accuracy of automatic noise calculation for several noise levels. The phantom consisted of objects with a very wide range of HU values, from -1000 to +950. The incremental value for each object was 10 HU. Each object had a size of 15 × 15 pixels separated by a distance of 5 pixels. There was no dominant homogeneous part in the phantom. The image of the phantom was then degraded to mimic the real image quality of CT by convolving it with a point spread function (PSF) and by addition of Gaussian noise. The magnitude of the Gaussian noises was varied (5, 10, 25, 50, 75 and 100 HUs), and they were considered as the ground truth noise (N_G). We also used a computational phantom with added actual noise from a CT scanner. The phantom was used to validate the automated noise measurement based on the average of the ten smallest standard deviations (SD) from the standard deviation map (SDM). Kernel sizes from 3 × 3 up to 27 × 27 pixels were examined in this study. A computational phantom for automated noise calculations validation has been successfully developed. It was found that the measured noise (N_M) was influenced by the kernel size. For kernels of 15 × 15 pixels or smaller, the N_Mvalue was much smaller than the N_G. For kernel sizes from 17 × 17 to 21 × 21 pixels, the N_Mvalue was about 90% of N_G. And for kernel sizes of 23 × 23 pixels and above, N_Mis greater than N_G. It was also found that even with small kernel sizes the relationship between N_Mand N_Gis linear with R²more than 0.995. Thus accurate noise levels can be automatically obtained even with small kernel sizes without any concern regarding the inhomogeneity of the object.

STIR-Net: Deep Spatial-Temporal Image Restoration Net for Radiation Reduction in CT Perfusion

Computed Tomography Perfusion (CTP) imaging is a cost-effective and fast approach to provide diagnostic images for acute stroke treatment. Its cine scanning mode allows the visualization of anatomic brain structures and blood flow; however, it requires contrast agent injection and continuous CT scanning over an extended time. In fact, the accumulative radiation dose to patients will increase health risks such as skin irritation, hair loss, cataract formation, and even cancer. Solutions for reducing radiation exposure include reducing the tube current and/or shortening the X-ray radiation exposure time. However, images scanned at lower tube currents are usually accompanied by higher levels of noise and artifacts. On the other hand, shorter X-ray radiation exposure time with longer scanning intervals will lead to image information that is insufficient to capture the blood flow dynamics between frames. Thus, it is critical for us to seek a solution that can preserve the image quality when the tube current and the temporal frequency are both low. We propose STIR-Net in this paper, an end-to-end spatial-temporal convolutional neural network structure, which exploits multi-directional automatic feature extraction and image reconstruction schema to recover high-quality CT slices effectively. With the inputs of low-dose and low-resolution patches at different cross-sections of the spatio-temporal data, STIR-Net blends the features from both spatial and temporal domains to reconstruct high-quality CT volumes. In this study, we finalize extensive experiments to appraise the image restoration performance at different levels of tube current and spatial and temporal resolution scales.The results demonstrate the capability of our STIR-Net to restore high-quality scans at as low as 11% of absorbed radiation dose of the current imaging protocol, yielding an average of 10% improvement for perfusion maps compared to the patch-based log likelihood method.

TENDER: Tensor non-local deconvolution enabled radiation reduction in CT perfusion

Stroke is the leading cause of long-term disability and the second leading cause of mortality in the world, and exerts an enormous burden on the public health. Computed Tomography (CT) remains one of the most widely used imaging modality for acute stroke diagnosis. However when coupled with CT perfusion, the excessive radiation exposure in repetitive imaging to assess treatment response and prognosis has raised significant public concerns regarding its potential hazards to both short- and long-term health outcomes. Tensor total variation has been proposed to reduce the necessary radiation dose in CT perfusion without comprising the image quality by fusing the information of the local anatomical structure with the temporal blood flow model. However the local search in the TTV framework fails to leverage the non-local information in the spatio-temporal data. In this paper, we propose TENDER, an efficient framework of non-local tensor deconvolution to maintain the accuracy of the hemodynamic parameters and the diagnostic reliability in low radiation dose CT perfusion. The tensor total variation is extended using non-local spatio-temporal cubics for regularization, and an efficient algorithm is proposed to reduce the time complexity with speedy similarity computation. Evaluations on clinical data of patients subjects with cerebrovascular disease and normal subjects demonstrate the advantage of non-local tensor deconvolution for reducing radiation dose in CT perfusion.

Low-Dose Volume-Perfusion CT of the Brain: Effects of Radiation Dose Reduction on Performance of Perfusion CT Algorithms

PURPOSE

We aimed to compare different computed tomography (CT) perfusion post-processing algorithms regarding image quality of perfusion maps from low-dose volume perfusion CT (VPCT) and their diagnostic performance regarding the detection of ischemic brain lesions.

METHODS AND MATERIALS

We included VPCT data of 21 patients with acute stroke (onset < 6h), which were acquired at 80 kV and 180 mAs. Low-dose VPCT datasets with 72 mAs (40 % of original dose) were generated using realistic low-dose simulation. Perfusion maps (cerebral blood volume (CBV); cerebral blood flow (CBF) from original and low-dose datasets were generated using two different commercially available post-processing methods: deconvolution-based method (DC) and maximum slope algorithm (MS). The resulting DC and MS perfusion maps were compared regarding perfusion values, signal-to-noise ratio (SNR) as well as image quality and diagnostic accuracy as rated by two blinded neuroradiologists.

RESULTS

Quantitative perfusion parameters highly correlated for both algorithms and both dose levels (r ≥ 0.613, p < 0.001). Regarding SNR levels and image quality of the CBV maps, no significant differences between DC and MS were found (p ≥ 0.683). Low-dose MS CBF maps yielded significantly higher SNR levels (p < 0.001) and quality scores (p = 0.014) than those of DC. Low-dose CBF and CBV maps from both DC and MS yielded high sensitivity and specificity for the detection of ischemic lesions (sensitivity ≥ 0.82, specificity ≥ 0.90).

CONCLUSION

Our results indicate that both methods produce diagnostically sufficient perfusion maps from simulated low-dose VPCT. However, MS produced CBF maps with significantly higher image quality and SNR than DC, indicating that MS might be more suitable for low-dose VPCT imaging.

Reduced-dose CT protocol for the assessment of cerebral vasospasm

INTRODUCTION

Despite the increased radiation dose, multimodal CT including noncontrast CT (NCT), CT angiography (CTA), and perfusion CT (PCT) remains a useful tool for the diagnosis of delayed cerebral ischemia (DCI) after aneurysmal subarachnoid hemorrhage (aSAH). The aim of this study was to assess the radiation dose and the image quality between a standard-dose and a reduced-dose multimodal CT protocol.

METHODS

The study group consisted of 26 aSAH patients with a suspicion of DCI on clinical examination and transcranial doppler. Two different CT protocols were used: a standard-dose protocol (NCT 120 kV, 350 mAs; CTA 100 kV, 250 mAs; PCT 80 kV, 200 mAs) from August 2011 to October 2013 (n = 13) and a reduced-dose protocol (NCT 100 kV, 400 mAs; CTA 100 kV, 220 mAs; PCT 80 kV, 180 mAs) from November 2013 to May 2014 (n = 13). Dose-length product (DLP), effective dose, volume CT dose index (CTDI), signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and overall image quality were determined for each examination.

RESULTS

The overall image quality was judged as good or excellent in all cases. The reduced-dose protocol allowed a 15 % decrease in both the median total DLP (2438 vs 2898 mGy cm, p < 0.0001) and the effective dose as well as a significant decrease in median CTDI of 23, 31, and 10 % for NCT, CTA, and CTP, respectively. This dose reduction did not result in significant alteration of SNR (except for NCT) or CNR between groups.

CONCLUSION

The present study showed that the reduced-dose multimodal CT protocol enabled a significant reduction of radiation dose without image quality impairment.

Robust Low-Dose CT Perfusion Deconvolution via Tensor Total-Variation Regularization

Acute brain diseases such as acute strokes and transit ischemic attacks are the leading causes of mortality and morbidity worldwide, responsible for 9% of total death every year. "Time is brain" is a widely accepted concept in acute cerebrovascular disease treatment. Efficient and accurate computational framework for hemodynamic parameters estimation can save critical time for thrombolytic therapy. Meanwhile the high level of accumulated radiation dosage due to continuous image acquisition in CT perfusion (CTP) raised concerns on patient safety and public health. However, low-radiation leads to increased noise and artifacts which require more sophisticated and time-consuming algorithms for robust estimation. In this paper, we focus on developing a robust and efficient framework to accurately estimate the perfusion parameters at low radiation dosage. Specifically, we present a tensor total-variation (TTV) technique which fuses the spatial correlation of the vascular structure and the temporal continuation of the blood signal flow. An efficient algorithm is proposed to find the solution with fast convergence and reduced computational complexity. Extensive evaluations are carried out in terms of sensitivity to noise levels, estimation accuracy, contrast preservation, and performed on digital perfusion phantom estimation, as well as in vivo clinical subjects. Our framework reduces the necessary radiation dose to only 8% of the original level and outperforms the state-of-art algorithms with peak signal-to-noise ratio improved by 32%. It reduces the oscillation in the residue functions, corrects over-estimation of cerebral blood flow (CBF) and under-estimation of mean transit time (MTT), and maintains the distinction between the deficit and normal regions.

Effects of radiation dose reduction in Volume Perfusion CT imaging of acute ischemic stroke

PURPOSE

To examine the influence of radiation dose reduction on image quality and sensitivity of Volume Perfusion CT (VPCT) maps regarding the detection of ischemic brain lesions.

METHODS AND MATERIALS

VPCT data of 20 patients with suspected ischemic stroke acquired at 80 kV and 180 mAs were included. Using realistic reduced-dose simulation, low-dose VPCT datasets with 144 mAs, 108 mAs, 72 mAs and 36 mAs (80 %, 60 %, 40 % and 20 % of the original levels) were generated, resulting in a total of 100 datasets. Perfusion maps were created and signal-to-noise-ratio (SNR) measurements were performed. Qualitative analyses were conducted by two blinded readers, who also assessed the presence/absence of ischemic lesions and scored CBV and CBF maps using a modified ASPECTS-score.

RESULTS

SNR of all low-dose datasets were significantly lower than those of the original datasets (p < .05). All datasets down to 72 mAs (40 %) yielded sufficient image quality and high sensitivity with excellent inter-observer-agreements, whereas 36 mAs datasets (20 %) yielded poor image quality in 15 % of the cases with lower sensitivity and inter-observer-agreements.

CONCLUSION

Low-dose VPCT using decreased tube currents down to 72 mAs (40 % of original radiation dose) produces sufficient perfusion maps for the detection of ischemic brain lesions.

KEY POINTS

• Perfusion CT is highly accurate for the detection of ischemic brain lesions • Perfusion CT results in high radiation exposure, therefore low-dose protocols are required • Reduction of tube current down to 72 mAs produces sufficient perfusion maps.

Improvement of image quality and radiation dose of CT perfusion of the brain by means of low-tube voltage (70 KV)

OBJECTIVES

To investigate the feasibility of 70 kV cerebral CT perfusion by comparing image quality and radiation exposure to 80 kV.

METHODS

Thirty patients with suspected cerebral ischemia who underwent dual-source CT perfusion were divided into group A (80 kV, 150 mAs) and group B (70 kV, 150 mAs). Quantitative comparisons were used for maximum enhancement, signal-to-noise index (SNI), and values of cerebral blood flow (CBF), cerebral blood flow (CBV), mean transit time (MTT) on CBF, CBV, and MTT images, and radiation dose from these two groups. Qualitative perfusion images were assessed by two readers.

RESULTS

Maximum enhancement for group B was higher than group A (P < 0.05). There were no significant differences between the two groups for SNI on CBF and CBV maps (P = 0.06 - 0.576), but significant differences for MTT when SNI was measured on frontal white matter and temporo-occipital white matter (P < 0.05). There were no differences among values of CBF, CBV, and MTT for both groups (P = 0.251-0.917). Mean image quality score in group B was higher than group A for CBF (P < 0.05), but no differences for CBV (P = 0.542) and MTT (P = 0.962). Radiation dose for group B decreased compared with group A.

CONCLUSIONS

70 kV cerebral CT perfusion reduces radiation dose without compromising image quality.

KEY POINTS

• Radiation dose is a key concern with the increased using cerebral CT perfusion. Cerebral CT perfusion of 70 kV reduces radiation dose without compromising image quality. • A 70-kV protocol could be used for cerebral CT perfusion.